OpenRaiser/Intern-Atlas huggingface dataset:
Rate-Splitting for Multi-Antenna Non-Orthogonal Unicast and Multicast Transmission
Venue: arXiv cs.IT 2018
Year: 2018

In a superimposed unicast and multicast transmission system, one layer of Successive Interference Cancellation (SIC) is required at each receiver to remove the multicast stream before decoding the unicast stream. In this paper, we show that a linearly-precoded Rate-Splitting (RS) strategy at the transmitter can efficiently exploit this existing SIC receiver architecture. By splitting the unicast message into common and private parts and encoding the common parts along with the multicast message into a super-common stream decoded by all users, the SIC is used for the dual purpose of separating the unicast and multicast streams as well as better managing the multi-user interference between the unicast streams. The precoders are designed with the objective of maximizing the Weighted Sum Rate (WSR) of the unicast messages subject to a Quality of Service (QoS) requirement of the multicast message and a sum power constraint. Numerical results show that RS outperforms existing Multi-User Linear-Precoding (MU-LP) and power-domain Non-Orthogonal Multiple Access (NOMA) in a wide range of user deployments (with a diversity of channel directions and channel strengths). Moreover, since one layer of SIC is required to separate the unicast and multicast streams, the performance gain of RS comes without any increase in the receiver complexity compared with MU-LP. Hence, in such non-orthogonal unicast and multicast transmissions, RS provides rate and QoS enhancements at no extra cost for the receivers.
An $O(1)$-Approximation Algorithm for Dynamic Weighted Vertex Cover with Soft Capacity
Venue: arXiv cs.DS 2018
Year: 2018

This study considers the (soft) capacitated vertex cover problem in a dynamic setting. This problem generalizes the dynamic model of the vertex cover problem, which has been intensively studied in recent years. Given a dynamically changing vertex-weighted graph $G=(V,E)$, which allows edge insertions and edge deletions, the goal is to design a data structure that maintains an approximate minimum vertex cover while satisfying the capacity constraint of each vertex. That is, when picking a copy of a vertex $v$ in the cover, the number of $v$'s incident edges covered by the copy is up to a given capacity of $v$. We extend Bhattacharya et al.'s work [SODA'15 and ICALP'15] to obtain a deterministic primal-dual algorithm for maintaining a constant-factor approximate minimum capacitated vertex cover with $O(\log n / ε)$ amortized update time, where $n$ is the number of vertices in the graph. The algorithm can be extended to (1) a more general model in which each edge is associated with a nonuniform and unsplittable demand, and (2) the more general capacitated set cover problem.
CNN+LSTM Architecture for Speech Emotion Recognition with Data Augmentation
Venue: arXiv cs.SD 2018
Year: 2018

In this work we design a neural network for recognizing emotions in speech, using the IEMOCAP dataset. Following the latest advances in audio analysis, we use an architecture involving both convolutional layers, for extracting high-level features from raw spectrograms, and recurrent ones for aggregating long-term dependencies. We examine the techniques of data augmentation with vocal track length perturbation, layer-wise optimizer adjustment, batch normalization of recurrent layers and obtain highly competitive results of 64.5% for weighted accuracy and 61.7% for unweighted accuracy on four emotions.
Event Nugget Detection with Forward-Backward Recurrent Neural Networks
Venue: arXiv cs.CL 2018
Year: 2018

Traditional event detection methods heavily rely on manually engineered rich features. Recent deep learning approaches alleviate this problem by automatic feature engineering. But such efforts, like tradition methods, have so far only focused on single-token event mentions, whereas in practice events can also be a phrase. We instead use forward-backward recurrent neural networks (FBRNNs) to detect events that can be either words or phrases. To the best our knowledge, this is one of the first efforts to handle multi-word events and also the first attempt to use RNNs for event detection. Experimental results demonstrate that FBRNN is competitive with the state-of-the-art methods on the ACE 2005 and the Rich ERE 2015 event detection tasks.
A Bio-inspired Redundant Sensing Architecture
Venue: arXiv cs.NE 2018
Year: 2018

Sensing is the process of deriving signals from the environment that allows artificial systems to interact with the physical world. The Shannon theorem specifies the maximum rate at which information can be acquired. However, this upper bound is hard to achieve in many man-made systems. The biological visual systems, on the other hand, have highly efficient signal representation and processing mechanisms that allow precise sensing. In this work, we argue that redundancy is one of the critical characteristics for such superior performance. We show architectural advantages by utilizing redundant sensing, including correction of mismatch error and significant precision enhancement. For a proof-of-concept demonstration, we have designed a heuristic-based analog-to-digital converter - a zero-dimensional quantizer. Through Monte Carlo simulation with the error probabilistic distribution as a priori, the performance approaching the Shannon limit is feasible. In actual measurements without knowing the error distribution, we observe at least 2-bit extra precision. The results may also help explain biological processes including the dominance of binocular vision, the functional roles of the fixational eye movements, and the structural mechanisms allowing hyperacuity.
Inverting The Generator Of A Generative Adversarial Network (II)
Venue: arXiv cs.CV 2018
Year: 2018

Generative adversarial networks (GANs) learn a deep generative model that is able to synthesise novel, high-dimensional data samples. New data samples are synthesised by passing latent samples, drawn from a chosen prior distribution, through the generative model. Once trained, the latent space exhibits interesting properties, that may be useful for down stream tasks such as classification or retrieval. Unfortunately, GANs do not offer an "inverse model", a mapping from data space back to latent space, making it difficult to infer a latent representation for a given data sample. In this paper, we introduce a technique, inversion, to project data samples, specifically images, to the latent space using a pre-trained GAN. Using our proposed inversion technique, we are able to identify which attributes of a dataset a trained GAN is able to model and quantify GAN performance, based on a reconstruction loss. We demonstrate how our proposed inversion technique may be used to quantitatively compare performance of various GAN models trained on three image datasets. We provide code for all of our experiments, https://github.com/ToniCreswell/InvertingGAN.
Framing Matters: Predicting Framing Changes and Legislation from Topic News Patterns
Venue: arXiv cs.CY 2018
Year: 2018

News has traditionally been well researched, with studies ranging from sentiment analysis to event detection and topic tracking. We extend the focus to two surprisingly under-researched aspects of news: \emph{framing} and \emph{predictive utility}. We demonstrate that framing influences public opinion and behavior, and present a simple entropic algorithm to characterize and detect framing changes. We introduce a dataset of news topics with framing changes, harvested from manual surveys in previous research. Our approach achieves an F-measure of $F_1=0.96$ on our data, whereas dynamic topic modeling returns $F_1=0.1$. We also establish that news has \emph{predictive utility}, by showing that legislation in topics of current interest can be foreshadowed and predicted from news patterns.
ASP:A Fast Adversarial Attack Example Generation Framework based on Adversarial Saliency Prediction
Venue: arXiv cs.CV 2018
Year: 2018

With the excellent accuracy and feasibility, the Neural Networks have been widely applied into the novel intelligent applications and systems. However, with the appearance of the Adversarial Attack, the NN based system performance becomes extremely vulnerable:the image classification results can be arbitrarily misled by the adversarial examples, which are crafted images with human unperceivable pixel-level perturbation. As this raised a significant system security issue, we implemented a series of investigations on the adversarial attack in this work: We first identify an image's pixel vulnerability to the adversarial attack based on the adversarial saliency analysis. By comparing the analyzed saliency map and the adversarial perturbation distribution, we proposed a new evaluation scheme to comprehensively assess the adversarial attack precision and efficiency. Then, with a novel adversarial saliency prediction method, a fast adversarial example generation framework, namely "ASP", is proposed with significant attack efficiency improvement and dramatic computation cost reduction. Compared to the previous methods, experiments show that ASP has at most 12 times speed-up for adversarial example generation, 2 times lower perturbation rate, and high attack success rate of 87% on both MNIST and Cifar10. ASP can be also well utilized to support the data-hungry NN adversarial training. By reducing the attack success rate as much as 90%, ASP can quickly and effectively enhance the defense capability of NN based system to the adversarial attacks.
Auto-Encoding Total Correlation Explanation
Venue: arXiv cs.LG 2018
Year: 2018

Advances in unsupervised learning enable reconstruction and generation of samples from complex distributions, but this success is marred by the inscrutability of the representations learned. We propose an information-theoretic approach to characterizing disentanglement and dependence in representation learning using multivariate mutual information, also called total correlation. The principle of total Cor-relation Ex-planation (CorEx) has motivated successful unsupervised learning applications across a variety of domains, but under some restrictive assumptions. Here we relax those restrictions by introducing a flexible variational lower bound to CorEx. Surprisingly, we find that this lower bound is equivalent to the one in variational autoencoders (VAE) under certain conditions. This information-theoretic view of VAE deepens our understanding of hierarchical VAE and motivates a new algorithm, AnchorVAE, that makes latent codes more interpretable through information maximization and enables generation of richer and more realistic samples.
A Comparison of Constraint Handling Techniques for Dynamic Constrained Optimization Problems
Venue: arXiv cs.NE 2018
Year: 2018

Dynamic constrained optimization problems (DCOPs) have gained researchers attention in recent years because a vast majority of real world problems change over time. There are studies about the effect of constrained handling techniques in static optimization problems. However, there lacks any substantial study in the behavior of the most popular constraint handling techniques when dealing with DCOPs. In this paper we study the four most popular used constraint handling techniques and apply a simple Differential Evolution (DE) algorithm coupled with a change detection mechanism to observe the behavior of these techniques. These behaviors were analyzed using a common benchmark to determine which techniques are suitable for the most prevalent types of DCOPs. For the purpose of analysis, common measures in static environments were adapted to suit dynamic environments. While an overall superior technique could not be determined, certain techniques outperformed others in different aspects like rate of optimization or reliability of solutions.
A Parameterized Strongly Polynomial Algorithm for Block Structured Integer Programs
Venue: arXiv cs.DS 2018
Year: 2018

The theory of $n$-fold integer programming has been recently emerging as an important tool in parameterized complexity. The input to an $n$-fold integer program (IP) consists of parameter $A$, dimension $n$, and numerical data of binary encoding length $L$. It was known for some time that such programs can be solved in polynomial time using $O(n^{g(A)}L)$ arithmetic operations where $g$ is an exponential function of the parameter. In 2013 it was shown that it can be solved in fixed-parameter tractable (FPT) time using $O(f(A)n^3L)$ arithmetic operations for a single-exponential function $f$. This, and a faster algorithm for a special case of combinatorial $n$-fold IP, have led to several very recent breakthroughs in the parameterized complexity of scheduling, stringology, and computational social choice. In 2015 it was shown that it can be solved in strongly polynomial time using $O(n^{g(A)})$ arithmetic operations. Here we establish a result which subsumes all three of the above results by showing that $n$-fold IP can be solved in strongly polynomial FPT time using $O(f(A)n^3)$ arithmetic operations. In fact, our results are much more general, briefly outlined as follows. - There is a strongly polynomial algorithm for ILP whenever a so-called Graver-best oracle is realizable for it. - Graver-best oracles for the large classes of multi-stage stochastic and tree-fold ILPs can be realized in FPT time. Together with the previous oracle algorithm, this newly shows two large classes of ILP to be strongly polynomial; in contrast, only few classes of ILP were previously known to be strongly polynomial. - We show that ILP is FPT parameterized by the largest coefficient $\|A\|_\infty$ and the primal or dual treedepth of $A$, and that this parameterization cannot be relaxed, signifying substantial progress in understanding the parameterized complexity of ILP.
A Calculus for Modeling Floating Authorizations
Venue: arXiv cs.PL 2018
Year: 2018

Controlling resource usage in distributed systems is a challenging task given the dynamics involved in access granting. Consider, for instance, the setting of floating licenses where access can be granted if the request originates in a licensed domain and the number of active users is within the license limits, and where licenses can be interchanged. Access granting in such scenarios is given in terms of floating authorizations, addressed in this paper as first class entities of a process calculus model, encompassing the notions of domain, accounting and delegation. We present the operational semantics of the model in two equivalent alternative ways, each informing on the specific nature of authorizations. We also introduce a typing discipline to single out systems that never get stuck due to lacking authorizations, addressing configurations where authorization assignment is not statically prescribed in the system specification.
Dealing with Uncertainties in User Feedback: Strategies Between Denying and Accepting
Venue: arXiv cs.HC 2018
Year: 2018

Latest research revealed a considerable lack of reliability within user feedback and discussed striking impacts for the assessment of adaptive web systems and content personalisation approaches, e.g. ranking errors, systematic biases to accuracy metrics as well as its natural offset (the magic barrier). In order to perform holistic assessments and to improve web systems, a variety of strategies have been proposed to deal with this so-called human uncertainty. In this contribution we discuss the most relevant strategies to handle uncertain feedback and demonstrate that these approaches are more or less ineffective to fulfil their objectives. In doing so, we consider human uncertainty within a purely probabilistic framework and utilise hypothesis testing as well as a generalisation of the magic barrier to compare the effects of recently proposed algorithms. On this basis we recommend a novel strategy of acceptance which turns away from mere filtering and discuss potential benefits for the community of the WWW.
How does undone science get funded? A bibliometric analysis linking rare diseases publications to national and European funding sources
Venue: arXiv cs.DL 2018
Year: 2018

One of the notable features of undone science debates is how formation of new interest groups becomes pivotal in mobilizing and championing emerging research on undone topics. Clearly money is one of the most important mediums through which different types of actors can support and steer scientists to work on undone topics. Yet which actors are more visible in their support for scientific research is something which has seldom been measured. This study delves into research funding in the context of rare diseases research, a topic which has evolved from the margins of medical research into a priority area articulated by many contemporary funding agencies. Rare diseases refer to conditions affecting relatively few people in a population. Given low incidences, interest groups have articulated a lack of attention within medical research compared to more common conditions. The rise to prominence of rare diseases in research funding policies is often explained in the science studies literature in terms of effective lobbying by social movements Likewise, innovative fundraising initiatives, infrastructure building, and close partnerships with research groups are other means through which interested actors have sought to build capacity for research into rare medical conditions. To date however systematic empirical evidence to compare the relative importance of different actors in funding rare disease research has not been produced. Building on interest in undone science in STS and science policy studies, our study hopes to map-out different kinds of funding actors and their influence on leading scientific research on rare diseases, by use of bibliometric tools. The approach we are developing relies on the use of Funding Acknowledgement data provided in Web of Science database.
Spectral Normalization for Generative Adversarial Networks
Venue: arXiv cs.LG 2018
Year: 2018

One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator. Our new normalization technique is computationally light and easy to incorporate into existing implementations. We tested the efficacy of spectral normalization on CIFAR10, STL-10, and ILSVRC2012 dataset, and we experimentally confirmed that spectrally normalized GANs (SN-GANs) is capable of generating images of better or equal quality relative to the previous training stabilization techniques.
Structured-based Curriculum Learning for End-to-end English-Japanese Speech Translation
Venue: arXiv cs.CL 2018
Year: 2018

Sequence-to-sequence attentional-based neural network architectures have been shown to provide a powerful model for machine translation and speech recognition. Recently, several works have attempted to extend the models for end-to-end speech translation task. However, the usefulness of these models were only investigated on language pairs with similar syntax and word order (e.g., English-French or English-Spanish). In this work, we focus on end-to-end speech translation tasks on syntactically distant language pairs (e.g., English-Japanese) that require distant word reordering. To guide the encoder-decoder attentional model to learn this difficult problem, we propose a structured-based curriculum learning strategy. Unlike conventional curriculum learning that gradually emphasizes difficult data examples, we formalize learning strategies from easier network structures to more difficult network structures. Here, we start the training with end-to-end encoder-decoder for speech recognition or text-based machine translation task then gradually move to end-to-end speech translation task. The experiment results show that the proposed approach could provide significant improvements in comparison with the one without curriculum learning.
Parameterized Algorithms for Zero Extension and Metric Labelling Problems
Venue: arXiv cs.DS 2018
Year: 2018

We consider the problems ZERO EXTENSION and METRIC LABELLING under the paradigm of parameterized complexity. These are natural, well-studied problems with important applications, but have previously not received much attention from parameterized complexity. Depending on the chosen cost function $μ$, we find that different algorithmic approaches can be applied to design FPT-algorithms: for arbitrary $μ$ we parameterized by the number of edges that cross the cut (not the cost) and show how to solve ZERO EXTENSION in time $O(|D|^{O(k^2)} n^4 \log n)$ using randomized contractions. We improve this running time with respect to both parameter and input size to $O(|D|^{O(k)} m)$ in the case where $μ$ is a metric. We further show that the problem admits a polynomial sparsifier, that is, a kernel of size $O(k^{|D|+1})$ that is independent of the metric $μ$. With the stronger condition that $μ$ is described by the distances of leaves in a tree, we parameterize by a gap parameter $(q - p)$ between the cost of a true solution $q$ and a `discrete relaxation' $p$ and achieve a running time of $O(|D|^{q-p} |T|m + |T|ϕ(n,m))$ where $T$ is the size of the tree over which $μ$ is defined and $ϕ(n,m)$ is the running time of a max-flow computation. We achieve a similar running for the more general METRIC LABELLING, while also allowing $μ$ to be the distance metric between an arbitrary subset of nodes in a tree using tools from the theory of VCSPs. We expect the methods used in the latter result to have further applications.
Finding The Greedy, Prodigal, and Suicidal Contracts at Scale
Venue: arXiv cs.CR 2018
Year: 2018

Smart contracts---stateful executable objects hosted on blockchains like Ethereum---carry billions of dollars worth of coins and cannot be updated once deployed. We present a new systematic characterization of a class of trace vulnerabilities, which result from analyzing multiple invocations of a contract over its lifetime. We focus attention on three example properties of such trace vulnerabilities: finding contracts that either lock funds indefinitely, leak them carelessly to arbitrary users, or can be killed by anyone. We implemented MAIAN, the first tool for precisely specifying and reasoning about trace properties, which employs inter-procedural symbolic analysis and concrete validator for exhibiting real exploits. Our analysis of nearly one million contracts flags 34,200 (2,365 distinct) contracts vulnerable, in 10 seconds per contract. On a subset of3,759 contracts which we sampled for concrete validation and manual analysis, we reproduce real exploits at a true positive rate of 89%, yielding exploits for3,686 contracts. Our tool finds exploits for the infamous Parity bug that indirectly locked 200 million dollars worth in Ether, which previous analyses failed to capture.
Code smells
Venue: arXiv cs.SE 2018
Year: 2018

Code smells as symptoms of poor design and implementation choices. Many times they are the result of so called technical debt. Our study showed that the interest in code smells research is increasing. However, most of the publications are appearing in conference proceedings. Most of the research is done in G7 and other highly developed countries. Four main research themes were identified namely code smell detection, bad smell based refactoring, software development and anti patterns. The results show that code smells can also have a positive connotation, we can develop software which smells good and attracts various customers and good smelling code could also serve as a pattern for future software development.